KEYWORDS: Data modeling, Radar, Data processing, Signal processing, Global Positioning System, Expectation maximization algorithms, Target detection, Data conversion, Binary data, Mathematical morphology
In this paper, the problem of detection, classification and tracking of highly manoeuvring boats in sea clutter is
considered. The considered problem is challenging due to numerous inherent issues: abrupt direction changes,
high level of false alarms, lowered detectability, group movement and re-grouping, among many others. The
results of applying a proposed measurement extraction and estimation technique to a set of real data from
DRDC-Ottawa trials using Ground Moving Target Indicator (GMTI) radar are described. Real radar data
containing a small manoeuvring boat in sea clutter is processed using Expectation Maximization (EM) Gaussian
Mixture Model (GMM) based estimation. A trial was undertaken to collect data against highly maneuvering
speedboats in the sea. All the data were collected in the GMTI single-channel high-resolution spotlight mode.
True data were collected using GPS recording equipment. Real data processing results are presented.
KEYWORDS: Unmanned aerial vehicles, Target detection, Sensors, Surveillance, Data fusion, Information fusion, Monte Carlo methods, Detection and tracking algorithms, Error analysis, Data processing
In this paper, we consider the problem of collaborative management of uninhabited aerial vehicles (UAVs) for multitarget tracking. In addition to providing a solution to the problem of controlling individual UAVs, we present a method for controlling the information flow among them. The latter provides a solution to one of the main
problems in decentralized tracking, namely, distributed information transfer and fusion among the participating platforms. The problem of decentralized cooperative control considered in this paper is an optimization of the information obtained by a number of UAVs, carrying out surveillance over a region, which includes a number
of confirmed and suspected moving targets with the goal to track confirmed targets and detects new targets in the area. Each UAV has to decide on the most optimal path with the objective to track as many targets as possible, maximizing the information obtained during its operation with the maximum possible accuracy at the
lowest possible cost. Limited communication between UAVs and uncertainty in the information obtained by each UAV regarding the location of the ground targets are addressed in the problem formulation. In order to handle these issues, the problem is presented as an operation of a group of decision makers. Markov Decision Processes (MDPs) are incorporated into the solution. A decision mechanism for collaborative distributed data fusion
provides each UAV with the required data for the fusion process while substantially reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of UAVs that are decentralized, heterogenous, and potentially unreliable. Simulation results are presented on a representative multisensor-multitarget tracking problem.
In this paper, we consider the problem of collaborative sensor management with particular application to using
unmanned aerial vehicles (UAVs) for multitarget tracking. The problem of decentralized cooperative control
considered in this paper is an optimization of the information obtained by a number of unmanned aerial vehicles
(UAVs) equipped with Ground Moving Target Indicator (GMTI) radars, carrying out surveillance over a region
which includes a number of confirmed and suspected moving targets. The goal is to track confirmed targets
and detect new targets in the area. Each UAV has to decide on the most optimal path with the objective to
track as many targets as possible maximizing the information obtained during its operation with the maximum
possible accuracy at the lowest possible cost. Limited communication between UAVs and uncertainty in the
information obtained by each UAV regarding the location of the ground targets are addressed in the problem
formulation. In order to handle these issues, the problem is presented as a decentralized operation of a group of
decision-makers lacking full observability of the global state of the system. Markov Decision Processes (MDPs)
are incorporated into the solution. Given the MDP model, a local policy of actions for a single agent (UAV) is
given by a mapping from a current partial view of a global state observed by an agent to actions. The available
probability model regarding possible and confirmed locations of the targets is considered in the computations
of the UAVs' policies. The authors present multi-level hierarchy of MDPs controlling each of the UAVs. Each
level in the hierarchy solves a problem at a different level of abstraction. Simulation results are presented on a
representative multisensor-multitarget tracking problem.
KEYWORDS: Probability theory, Data fusion, Sensors, Data modeling, Defense and security, Information fusion, Distributed computing, Chemical elements, Systems modeling, Fuzzy logic
This invited panel discussion "Issues and challenges in uncertainty representation and management with applications to real-world problems" includes viewgraphs and presentation papers on these topics--Research challenges: dependence issues in feature/declaration
data fusion; Conceptual and methodological issues
in evidential reasoning; The uncertainty and knowledge challenge in distributed systems: an information fusion standpoint; Statistical modeling and management of uncertainty: a position paper; On conditioning in the Dempster-Shafer context; Dempster-Shafer theory made tractable and stable; and Collaborative distributed data fusion architecture using multi-level Markov decision processes.
In this paper, we consider the problem of collaborative sensor management with particular application to using
unmanned aerial vehicles (UAVs) for multitarget tracking. We study the problem of decentralized cooperative
control of a group of UAVs carrying out surveillance over a region that includes a number of moving targets. The
objective is to maximize the information obtained and to track as many targets as possible with the maximum
possible accuracy. Uncertainty in the information obtained by each UAV regarding the location of the ground
targets are addressed in the problem formulation. In order to handle these issues, the problem is presented
as a decentralized operation of a group of decision-makers lacking full observability of the global state of the
system. Recent advances in solving special classes of decentralized Markov Decision Processes (Dec-MDPs)
are incorporated into the solution. In these classes of Dec-MDPs, the agents' transitions and observations are
independent. Also, the collaborating agents share common goals or objectives. Given the Dec-MDP model, a
local policy of actions for a single agent (UAV) is given by a mapping from a current partial view of a global state
observed by an agent to actions. The available probability model regarding possible and confirmed locations
of the targets is considered in the computations of the UAVs' policies. Simulation results are presented on a
representative multisensor-multitarget tracking problem.
KEYWORDS: Sensors, Data fusion, Detection and tracking algorithms, Environmental sensing, Data centers, Process modeling, Radar, Algorithm development, Surveillance, Kinematics
In this paper we present the development of a multisensor-multitarget tracking testbed for large-scale distributed (or network-centric) scenarios. The project, which is in progress at McMaster University and the Royal Military College of Canada, is supported by the Department of National Defence and Raytheon Canada. The objective is to develop a testbed capable of handling multiple, heterogeneous sensors in a hierarchical architecture for maritime surveillance. The testbed consists of a scenario generator that can generate simulated data from multiple sensors including radar, sonar, IR and ESM as well as a tracker framework into which different tracking algorithms can be integrated. In the first stage of the project, the IMM/Assignment tracker, and the Particle Filter (PF) tracker are implemented in a distributed architecture and some preliminary results are obtained. Other trackers like the Multiple Hypothesis Tracker (MHT) are also planned for the future.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.